{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('601933')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     601933.SH   20241225   6.02   6.64   5.82   6.43       6.11    0.32   \n",
      "1     601933.SH   20241224   5.90   6.29   5.73   6.11       5.94    0.17   \n",
      "2     601933.SH   20241223   6.25   6.33   5.91   5.94       6.22   -0.28   \n",
      "3     601933.SH   20241220   6.31   6.50   6.20   6.22       6.26   -0.04   \n",
      "4     601933.SH   20241219   6.60   6.60   6.20   6.26       6.87   -0.61   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3381  601933.SH   20101221  36.50  36.94  34.96  36.27      36.23    0.04   \n",
      "3382  601933.SH   20101220  36.58  37.28  35.00  36.23      37.71   -1.48   \n",
      "3383  601933.SH   20101217  35.68  37.71  34.71  37.71      34.28    3.43   \n",
      "3384  601933.SH   20101216  32.95  35.46  32.51  34.28      32.29    1.99   \n",
      "3385  601933.SH   20101215  32.03  34.18  31.71  32.29      23.98    8.31   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      5.2373  7040319.45  4376640.020  \n",
      "1      2.8620  5980441.74  3586005.454  \n",
      "2     -4.5016  5370363.65  3282422.385  \n",
      "3     -0.6390  5479945.91  3472134.846  \n",
      "4     -8.8792  7177953.79  4582664.992  \n",
      "...       ...         ...          ...  \n",
      "3381   0.1100   169668.98   609047.559  \n",
      "3382  -3.9200   254976.41   926433.466  \n",
      "3383  10.0100   301569.08  1092355.518  \n",
      "3384   6.1600   497805.40  1676823.150  \n",
      "3385  34.6500   763489.65  2490256.157  \n",
      "\n",
      "[3386 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('1272de974922dba228858095d2c28002a74de242a4b4557d74bdbedc')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"601933.SH\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     601933.SH   20241225   6.02   6.64   5.82   6.43       6.11    0.32   \n",
      "1     601933.SH   20241224   5.90   6.29   5.73   6.11       5.94    0.17   \n",
      "2     601933.SH   20241223   6.25   6.33   5.91   5.94       6.22   -0.28   \n",
      "3     601933.SH   20241220   6.31   6.50   6.20   6.22       6.26   -0.04   \n",
      "4     601933.SH   20241219   6.60   6.60   6.20   6.26       6.87   -0.61   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3381  601933.SH   20101221  36.50  36.94  34.96  36.27      36.23    0.04   \n",
      "3382  601933.SH   20101220  36.58  37.28  35.00  36.23      37.71   -1.48   \n",
      "3383  601933.SH   20101217  35.68  37.71  34.71  37.71      34.28    3.43   \n",
      "3384  601933.SH   20101216  32.95  35.46  32.51  34.28      32.29    1.99   \n",
      "3385  601933.SH   20101215  32.03  34.18  31.71  32.29      23.98    8.31   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      5.2373  7040319.45  4376640.020  \n",
      "1      2.8620  5980441.74  3586005.454  \n",
      "2     -4.5016  5370363.65  3282422.385  \n",
      "3     -0.6390  5479945.91  3472134.846  \n",
      "4     -8.8792  7177953.79  4582664.992  \n",
      "...       ...         ...          ...  \n",
      "3381   0.1100   169668.98   609047.559  \n",
      "3382  -3.9200   254976.41   926433.466  \n",
      "3383  10.0100   301569.08  1092355.518  \n",
      "3384   6.1600   497805.40  1676823.150  \n",
      "3385  34.6500   763489.65  2490256.157  \n",
      "\n",
      "[3386 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601933.csv\")\n",
    "stockname='永辉超市'\n",
    "stockcode='601933'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3376</th>\n",
       "      <th>3377</th>\n",
       "      <th>3378</th>\n",
       "      <th>3379</th>\n",
       "      <th>3380</th>\n",
       "      <th>3381</th>\n",
       "      <th>3382</th>\n",
       "      <th>3383</th>\n",
       "      <th>3384</th>\n",
       "      <th>3385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>...</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "      <td>601933.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20101228</td>\n",
       "      <td>20101227</td>\n",
       "      <td>20101224</td>\n",
       "      <td>20101223</td>\n",
       "      <td>20101222</td>\n",
       "      <td>20101221</td>\n",
       "      <td>20101220</td>\n",
       "      <td>20101217</td>\n",
       "      <td>20101216</td>\n",
       "      <td>20101215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>6.02</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.31</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.19</td>\n",
       "      <td>7.15</td>\n",
       "      <td>7.16</td>\n",
       "      <td>6.77</td>\n",
       "      <td>6.11</td>\n",
       "      <td>...</td>\n",
       "      <td>32.59</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>34.51</td>\n",
       "      <td>35.7</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.58</td>\n",
       "      <td>35.68</td>\n",
       "      <td>32.95</td>\n",
       "      <td>32.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>6.64</td>\n",
       "      <td>6.29</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.62</td>\n",
       "      <td>6.94</td>\n",
       "      <td>...</td>\n",
       "      <td>32.77</td>\n",
       "      <td>34.42</td>\n",
       "      <td>34.79</td>\n",
       "      <td>35.35</td>\n",
       "      <td>36.18</td>\n",
       "      <td>36.94</td>\n",
       "      <td>37.28</td>\n",
       "      <td>37.71</td>\n",
       "      <td>35.46</td>\n",
       "      <td>34.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>5.82</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.91</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.19</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.1</td>\n",
       "      <td>...</td>\n",
       "      <td>30.6</td>\n",
       "      <td>32.8</td>\n",
       "      <td>32.6</td>\n",
       "      <td>34.18</td>\n",
       "      <td>34.54</td>\n",
       "      <td>34.96</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.71</td>\n",
       "      <td>32.51</td>\n",
       "      <td>31.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>6.43</td>\n",
       "      <td>6.11</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.22</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.87</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.94</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75</td>\n",
       "      <td>33.06</td>\n",
       "      <td>33.08</td>\n",
       "      <td>34.41</td>\n",
       "      <td>34.75</td>\n",
       "      <td>36.27</td>\n",
       "      <td>36.23</td>\n",
       "      <td>37.71</td>\n",
       "      <td>34.28</td>\n",
       "      <td>32.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>6.11</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.22</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.87</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.94</td>\n",
       "      <td>6.31</td>\n",
       "      <td>...</td>\n",
       "      <td>33.06</td>\n",
       "      <td>33.08</td>\n",
       "      <td>34.41</td>\n",
       "      <td>34.75</td>\n",
       "      <td>36.27</td>\n",
       "      <td>36.23</td>\n",
       "      <td>37.71</td>\n",
       "      <td>34.28</td>\n",
       "      <td>32.29</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.99</td>\n",
       "      <td>8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>5.2373</td>\n",
       "      <td>2.862</td>\n",
       "      <td>-4.5016</td>\n",
       "      <td>-0.639</td>\n",
       "      <td>-8.8792</td>\n",
       "      <td>-0.1453</td>\n",
       "      <td>-9.9476</td>\n",
       "      <td>6.8531</td>\n",
       "      <td>3.0259</td>\n",
       "      <td>9.9842</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.99</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-3.92</td>\n",
       "      <td>10.01</td>\n",
       "      <td>6.16</td>\n",
       "      <td>34.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>7040319.45</td>\n",
       "      <td>5980441.74</td>\n",
       "      <td>5370363.65</td>\n",
       "      <td>5479945.91</td>\n",
       "      <td>7177953.79</td>\n",
       "      <td>9658177.74</td>\n",
       "      <td>4446688.85</td>\n",
       "      <td>13917606.4</td>\n",
       "      <td>13703780.63</td>\n",
       "      <td>11459897.86</td>\n",
       "      <td>...</td>\n",
       "      <td>88401.39</td>\n",
       "      <td>107039.04</td>\n",
       "      <td>113126.14</td>\n",
       "      <td>92538.66</td>\n",
       "      <td>127442.04</td>\n",
       "      <td>169668.98</td>\n",
       "      <td>254976.41</td>\n",
       "      <td>301569.08</td>\n",
       "      <td>497805.4</td>\n",
       "      <td>763489.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>4376640.02</td>\n",
       "      <td>3586005.454</td>\n",
       "      <td>3282422.385</td>\n",
       "      <td>3472134.846</td>\n",
       "      <td>4582664.992</td>\n",
       "      <td>6302801.116</td>\n",
       "      <td>3110376.532</td>\n",
       "      <td>10579162.148</td>\n",
       "      <td>9621556.563</td>\n",
       "      <td>7738764.842</td>\n",
       "      <td>...</td>\n",
       "      <td>278855.392</td>\n",
       "      <td>358996.021</td>\n",
       "      <td>378172.899</td>\n",
       "      <td>321036.178</td>\n",
       "      <td>450628.53</td>\n",
       "      <td>609047.559</td>\n",
       "      <td>926433.466</td>\n",
       "      <td>1092355.518</td>\n",
       "      <td>1676823.15</td>\n",
       "      <td>2490256.157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3386 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0            1            2            3            4     \\\n",
       "ts_code      601933.SH    601933.SH    601933.SH    601933.SH    601933.SH   \n",
       "trade_date    20241225     20241224     20241223     20241220     20241219   \n",
       "open              6.02          5.9         6.25         6.31          6.6   \n",
       "high              6.64         6.29         6.33          6.5          6.6   \n",
       "low               5.82         5.73         5.91          6.2          6.2   \n",
       "close             6.43         6.11         5.94         6.22         6.26   \n",
       "pre_close         6.11         5.94         6.22         6.26         6.87   \n",
       "change            0.32         0.17        -0.28        -0.04        -0.61   \n",
       "pct_chg         5.2373        2.862      -4.5016       -0.639      -8.8792   \n",
       "vol         7040319.45   5980441.74   5370363.65   5479945.91   7177953.79   \n",
       "amount      4376640.02  3586005.454  3282422.385  3472134.846  4582664.992   \n",
       "\n",
       "                   5            6             7            8            9     \\\n",
       "ts_code       601933.SH    601933.SH     601933.SH    601933.SH    601933.SH   \n",
       "trade_date     20241218     20241217      20241216     20241213     20241212   \n",
       "open               6.19         7.15          7.16         6.77         6.11   \n",
       "high               6.88         7.35          7.87         7.62         6.94   \n",
       "low                6.19         6.88          6.89         6.66          6.1   \n",
       "close              6.87         6.88          7.64         7.15         6.94   \n",
       "pre_close          6.88         7.64          7.15         6.94         6.31   \n",
       "change            -0.01        -0.76          0.49         0.21         0.63   \n",
       "pct_chg         -0.1453      -9.9476        6.8531       3.0259       9.9842   \n",
       "vol          9658177.74   4446688.85    13917606.4  13703780.63  11459897.86   \n",
       "amount      6302801.116  3110376.532  10579162.148  9621556.563  7738764.842   \n",
       "\n",
       "            ...        3376        3377        3378        3379       3380  \\\n",
       "ts_code     ...   601933.SH   601933.SH   601933.SH   601933.SH  601933.SH   \n",
       "trade_date  ...    20101228    20101227    20101224    20101223   20101222   \n",
       "open        ...       32.59        33.0        34.2       34.51       35.7   \n",
       "high        ...       32.77       34.42       34.79       35.35      36.18   \n",
       "low         ...        30.6        32.8        32.6       34.18      34.54   \n",
       "close       ...       30.75       33.06       33.08       34.41      34.75   \n",
       "pre_close   ...       33.06       33.08       34.41       34.75      36.27   \n",
       "change      ...       -2.31       -0.02       -1.33       -0.34      -1.52   \n",
       "pct_chg     ...       -6.99       -0.06       -3.87       -0.98      -4.19   \n",
       "vol         ...    88401.39   107039.04   113126.14    92538.66  127442.04   \n",
       "amount      ...  278855.392  358996.021  378172.899  321036.178  450628.53   \n",
       "\n",
       "                  3381        3382         3383        3384         3385  \n",
       "ts_code      601933.SH   601933.SH    601933.SH   601933.SH    601933.SH  \n",
       "trade_date    20101221    20101220     20101217    20101216     20101215  \n",
       "open              36.5       36.58        35.68       32.95        32.03  \n",
       "high             36.94       37.28        37.71       35.46        34.18  \n",
       "low              34.96        35.0        34.71       32.51        31.71  \n",
       "close            36.27       36.23        37.71       34.28        32.29  \n",
       "pre_close        36.23       37.71        34.28       32.29        23.98  \n",
       "change            0.04       -1.48         3.43        1.99         8.31  \n",
       "pct_chg           0.11       -3.92        10.01        6.16        34.65  \n",
       "vol          169668.98   254976.41    301569.08    497805.4    763489.65  \n",
       "amount      609047.559  926433.466  1092355.518  1676823.15  2490256.157  \n",
       "\n",
       "[11 rows x 3386 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x284c6592bd0>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601933.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3356 entries, 0 to 3355\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3356 non-null   datetime64[ns]\n",
      " 1   open        3356 non-null   float64       \n",
      " 2   high        3356 non-null   float64       \n",
      " 3   low         3356 non-null   float64       \n",
      " 4   close       3356 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 131.2 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3351</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.960</td>\n",
       "      <td>6.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3352</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>6.31</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.22</td>\n",
       "      <td>6.774</td>\n",
       "      <td>6.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3353</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.33</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.434</td>\n",
       "      <td>6.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>5.90</td>\n",
       "      <td>6.29</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.280</td>\n",
       "      <td>6.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.64</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.43</td>\n",
       "      <td>6.192</td>\n",
       "      <td>6.644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3351 2024-12-19  6.60  6.60  6.20   6.26  6.960  6.518\n",
       "3352 2024-12-20  6.31  6.50  6.20   6.22  6.774  6.578\n",
       "3353 2024-12-23  6.25  6.33  5.91   5.94  6.434  6.624\n",
       "3354 2024-12-24  5.90  6.29  5.73   6.11  6.280  6.632\n",
       "3355 2024-12-25  6.02  6.64  5.82   6.43  6.192  6.644"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-12-15  32.03  34.18  31.71  32.29\n",
      "1    2010-12-16  32.95  35.46  32.51  34.28\n",
      "2    2010-12-17  35.68  37.71  34.71  37.71\n",
      "3    2010-12-20  36.58  37.28  35.00  36.23\n",
      "4    2010-12-21  36.50  36.94  34.96  36.27\n",
      "...         ...    ...    ...    ...    ...\n",
      "3381 2024-12-19   6.60   6.60   6.20   6.26\n",
      "3382 2024-12-20   6.31   6.50   6.20   6.22\n",
      "3383 2024-12-23   6.25   6.33   5.91   5.94\n",
      "3384 2024-12-24   5.90   6.29   5.73   6.11\n",
      "3385 2024-12-25   6.02   6.64   5.82   6.43\n",
      "\n",
      "[3386 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3386 entries, 0 to 3385\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3386 non-null   datetime64[ns]\n",
      " 1   open        3386 non-null   float64       \n",
      " 2   high        3386 non-null   float64       \n",
      " 3   low         3386 non-null   float64       \n",
      " 4   close       3386 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 132.4 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-12-15</td>\n",
       "      <td>32.03</td>\n",
       "      <td>34.18</td>\n",
       "      <td>31.71</td>\n",
       "      <td>32.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-12-16</td>\n",
       "      <td>32.95</td>\n",
       "      <td>35.46</td>\n",
       "      <td>32.51</td>\n",
       "      <td>34.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-12-17</td>\n",
       "      <td>35.68</td>\n",
       "      <td>37.71</td>\n",
       "      <td>34.71</td>\n",
       "      <td>37.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-12-20</td>\n",
       "      <td>36.58</td>\n",
       "      <td>37.28</td>\n",
       "      <td>35.00</td>\n",
       "      <td>36.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-12-21</td>\n",
       "      <td>36.50</td>\n",
       "      <td>36.94</td>\n",
       "      <td>34.96</td>\n",
       "      <td>36.27</td>\n",
       "      <td>35.356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.960</td>\n",
       "      <td>6.518</td>\n",
       "      <td>5.436333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>6.31</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.22</td>\n",
       "      <td>6.774</td>\n",
       "      <td>6.578</td>\n",
       "      <td>5.472333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.33</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.434</td>\n",
       "      <td>6.624</td>\n",
       "      <td>5.504000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>5.90</td>\n",
       "      <td>6.29</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.280</td>\n",
       "      <td>6.632</td>\n",
       "      <td>5.535333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.64</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.43</td>\n",
       "      <td>6.192</td>\n",
       "      <td>6.644</td>\n",
       "      <td>5.586333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3386 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5     10        30\n",
       "0    2010-12-15  32.03  34.18  31.71  32.29     NaN    NaN       NaN\n",
       "1    2010-12-16  32.95  35.46  32.51  34.28     NaN    NaN       NaN\n",
       "2    2010-12-17  35.68  37.71  34.71  37.71     NaN    NaN       NaN\n",
       "3    2010-12-20  36.58  37.28  35.00  36.23     NaN    NaN       NaN\n",
       "4    2010-12-21  36.50  36.94  34.96  36.27  35.356    NaN       NaN\n",
       "...         ...    ...    ...    ...    ...     ...    ...       ...\n",
       "3381 2024-12-19   6.60   6.60   6.20   6.26   6.960  6.518  5.436333\n",
       "3382 2024-12-20   6.31   6.50   6.20   6.22   6.774  6.578  5.472333\n",
       "3383 2024-12-23   6.25   6.33   5.91   5.94   6.434  6.624  5.504000\n",
       "3384 2024-12-24   5.90   6.29   5.73   6.11   6.280  6.632  5.535333\n",
       "3385 2024-12-25   6.02   6.64   5.82   6.43   6.192  6.644  5.586333\n",
       "\n",
       "[3386 rows x 8 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           6.02   6.64\n",
       "1           5.90   6.29\n",
       "2           6.25   6.33\n",
       "3           6.31   6.50\n",
       "4           6.60   6.60\n",
       "...          ...    ...\n",
       "3381       36.50  36.94\n",
       "3382       36.58  37.28\n",
       "3383       35.68  37.71\n",
       "3384       32.95  35.46\n",
       "3385       32.03  34.18\n",
       "\n",
       "[3386 rows x 2 columns]>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32.29],\n",
       "       [34.28],\n",
       "       [37.71],\n",
       "       ...,\n",
       "       [ 5.94],\n",
       "       [ 6.11],\n",
       "       [ 6.43]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14958.0</th>\n",
       "      <td>32.03</td>\n",
       "      <td>34.18</td>\n",
       "      <td>31.71</td>\n",
       "      <td>32.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14959.0</th>\n",
       "      <td>32.95</td>\n",
       "      <td>35.46</td>\n",
       "      <td>32.51</td>\n",
       "      <td>34.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14960.0</th>\n",
       "      <td>35.68</td>\n",
       "      <td>37.71</td>\n",
       "      <td>34.71</td>\n",
       "      <td>37.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14963.0</th>\n",
       "      <td>36.58</td>\n",
       "      <td>37.28</td>\n",
       "      <td>35.00</td>\n",
       "      <td>36.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14964.0</th>\n",
       "      <td>36.50</td>\n",
       "      <td>36.94</td>\n",
       "      <td>34.96</td>\n",
       "      <td>36.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>6.60</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>6.31</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>6.25</td>\n",
       "      <td>6.33</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>5.90</td>\n",
       "      <td>6.29</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>6.02</td>\n",
       "      <td>6.64</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3386 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14958.0  32.03  34.18  31.71  32.29\n",
       "14959.0  32.95  35.46  32.51  34.28\n",
       "14960.0  35.68  37.71  34.71  37.71\n",
       "14963.0  36.58  37.28  35.00  36.23\n",
       "14964.0  36.50  36.94  34.96  36.27\n",
       "...        ...    ...    ...    ...\n",
       "20076.0   6.60   6.60   6.20   6.26\n",
       "20077.0   6.31   6.50   6.20   6.22\n",
       "20080.0   6.25   6.33   5.91   5.94\n",
       "20081.0   5.90   6.29   5.73   6.11\n",
       "20082.0   6.02   6.64   5.82   6.43\n",
       "\n",
       "[3386 rows x 4 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14958.0</td>\n",
       "      <td>32.03</td>\n",
       "      <td>34.18</td>\n",
       "      <td>31.71</td>\n",
       "      <td>32.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14959.0</td>\n",
       "      <td>32.95</td>\n",
       "      <td>35.46</td>\n",
       "      <td>32.51</td>\n",
       "      <td>34.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14960.0</td>\n",
       "      <td>35.68</td>\n",
       "      <td>37.71</td>\n",
       "      <td>34.71</td>\n",
       "      <td>37.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14963.0</td>\n",
       "      <td>36.58</td>\n",
       "      <td>37.28</td>\n",
       "      <td>35.00</td>\n",
       "      <td>36.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14964.0</td>\n",
       "      <td>36.50</td>\n",
       "      <td>36.94</td>\n",
       "      <td>34.96</td>\n",
       "      <td>36.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>6.31</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.33</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>5.90</td>\n",
       "      <td>6.29</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.64</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3386 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14958.0  32.03  34.18  31.71  32.29\n",
       "1     14959.0  32.95  35.46  32.51  34.28\n",
       "2     14960.0  35.68  37.71  34.71  37.71\n",
       "3     14963.0  36.58  37.28  35.00  36.23\n",
       "4     14964.0  36.50  36.94  34.96  36.27\n",
       "...       ...    ...    ...    ...    ...\n",
       "3381  20076.0   6.60   6.60   6.20   6.26\n",
       "3382  20077.0   6.31   6.50   6.20   6.22\n",
       "3383  20080.0   6.25   6.33   5.91   5.94\n",
       "3384  20081.0   5.90   6.29   5.73   6.11\n",
       "3385  20082.0   6.02   6.64   5.82   6.43\n",
       "\n",
       "[3386 rows x 5 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x284c67c35d0>]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\张哲凯\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_6 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_7 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1273 - val_loss: 0.0217\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0187 - val_loss: 4.8799e-04\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 - val_loss: 7.1476e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3392e-04 - val_loss: 5.0004e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.2161e-04 - val_loss: 4.0276e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.7929e-04 - val_loss: 3.7564e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.6577e-04 - val_loss: 3.6540e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.6060e-04 - val_loss: 3.6026e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5727e-04 - val_loss: 3.5658e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5510e-04 - val_loss: 3.5438e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.5393e-04 - val_loss: 3.5406e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5373e-04 - val_loss: 3.5431e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5449e-04 - val_loss: 3.5593e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5539e-04 - val_loss: 3.5884e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.5673e-04 - val_loss: 3.6265e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5784e-04 - val_loss: 3.6731e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5825e-04 - val_loss: 3.7134e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5788e-04 - val_loss: 3.7643e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5700e-04 - val_loss: 3.8033e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5542e-04 - val_loss: 3.8437e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5339e-04 - val_loss: 3.8947e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5118e-04 - val_loss: 3.9432e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4856e-04 - val_loss: 3.9844e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4551e-04 - val_loss: 4.0347e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4182e-04 - val_loss: 4.0661e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.3748e-04 - val_loss: 4.0834e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.3282e-04 - val_loss: 4.1107e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.2761e-04 - val_loss: 4.1037e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.2222e-04 - val_loss: 4.0800e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.1620e-04 - val_loss: 4.0543e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_6 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_7 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 12 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x00000284D4F4AFC0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 139ms/stepWARNING:tensorflow:6 out of the last 13 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x00000284D4F4AFC0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.46017\n",
      "均方根误差: 0.67836\n",
      "平均绝对误差: 0.53115\n",
      "R2: 0.51125\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
